Recent developments have utilized CBN and CIN for integrating text information into synthesis. The Semantic-Spatial aware version of CBN has been proposed by SSGAN [1]} as a way of making CBN aware of spatial regions relevant to text description. To better fuse text information into the synthesis process,
DF-GAN [2]} proposed a deep fusion method that have multiple affine layers in a single block. Different from previous work, DF-GAN discards normalization operation without decrease in performance, which reduces computational occupation and limitation from large batch-size. Similarly to DF-GAN, our RAT also discards normalization operations since normalization has no influence on performance.
